A role for bacterial urease in gut dysbiosis and Crohn's disease

Abstract

Gut dysbiosis during inflammatory bowel disease involves alterations in the gut microbiota associated with inflammation of the host gut. We used a combination of shotgun metagenomic sequencing and metabolomics to analyze fecal samples from pediatric patients with Crohn's disease and found an association between disease severity, gut dysbiosis, and bacterial production of free amino acids. Nitrogen flux studies using 15N in mice showed that activity of bacterial urease, an enzyme that releases ammonia by hydrolysis of host urea, led to the transfer of murine host-derived nitrogen to the gut microbiota where it was used for amino acid synthesis. Inoculation of a conventional murine host (pretreated with antibiotics and polyethylene glycol) with commensal Escherichia coli engineered to express urease led to dysbiosis of the gut microbiota, resulting in a predominance of Proteobacteria species. This was associated with a worsening of immune-mediated colitis in these animals. A potential role for altered urease expression and nitrogen flux in the development of gut dysbiosis suggests that bacterial urease may be a potential therapeutic target for inflammatory bowel diseases.

Conflict of interest statement

Competing interests: All other authors declare that they have no competing interests.

Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Figures

Fig. 1. Associations between fecal amino acids and their derivatives with disease in pediatric patients with Crohn’s disease compared to healthy subjects

(A) Associations with metabolic pathways as determined by Wilcoxon rank-sum test, with FDR-adjusted P < 0.05 as the cutoff. Bars indicate the number of metabolites in each category, as defined by the Human Metabolome Database (56). (B) A heat map demonstrating relative abundance of fecal amino acids and their derivatives before therapy according to the presence of disease, cluster assignment, fecal calprotectin concentration, use of antibiotics and corticosteroids, response or no response to therapy, and treatment assignment. Metadata are indicated by the color code at the top of the figure. Metabolite categories are color-coded along the left side of the heat map. White cells indicate missing data. All metabolites that were statistically different (FDR-adjusted P < 0.05) in abundance between Crohn’s disease samples and healthy controls are marked with an asterisk (*); metabolites differing significantly (FDR-adjusted P < 0.05) between cluster 1 and cluster 2 (previously defined clusters based on bacterial abundances in which cluster 1 resembled the healthy controls and cluster 2 was dysbiotic) are marked by a plus sign (+). The clusters were defined by partitioning around medoids (a centroid within a data set whose average dissimilarity to all the data points in the cluster is minimal) with estimation of number of clusters [PAMK (partitioning around medoids with estimation of number of clusters)], as previously described (5). EEN, exclusive enteral nutrition; PEN, partial enteral nutrition; FCP, fecal calprotectin; SDMA, symmetric dimethylarginine; ADMA, asymmetric dimethylarginine; GABA, γ-aminobutyric acid; NMMA, NG-methyl-l-arginine.

Fig. 2. Associations between bacterial taxa abundance ascertained by fecal shotgun metagenomic sequencing and the fecal metabolomein healthy pediatric subjects and those with Crohn’s disease

Spearman rank correlation with the direction and strength of association indicated by the color index in the figure. Only metabolites classified as amino acids and their derivatives are shown. Statistically significant correlations after adjusting for healthy and disease status (FDR

Fig. 3. In vivo heavy isotope assays using 15N-labeled urea to determine the effect of bacterial urease on nitrogen flux in the murine gut microbiota

(A) 16S ribosomal RNA (rRNA) gene-tagged sequencing showing effective engraftment of the murine host with ASF after 3 days after completion of ASF inoculation (after ASF transplant) and 8 days after initiation of [15N2]urea administration (after [15N2]urea). (B) Atom percent excess (APE) of 15N in fecal lysine for mice with a normal commensal microbiota (control), a minimal urease microbiota (ASF-transplanted), and a substantially reduced microbiota biomass due to antibiotic treatment. Mice were given 15N-labeled urea via oral gavage. n = 5 per group, means ± SD; statistical analysis performed by two-tailed Student’s t tests, *P < 0.05.

Fig. 4. Specific antibiotics reduce fecal biomass in mice and humans

(A) Unweighted UniFrac principal coordinates analysis of the murine fecal microbiota at baseline (day 0) and 30 days after treatment of mice orally with two nonabsorbed antibiotics (vancomycin and neomycin), PEG, or a combination of both (ABX/PEG). (B) Fecal bacterial load quantified by 16S rRNA gene copy number polymerase chain reaction (PCR) in five healthy human subjects at baseline and days 2, 3, 4, 8, and 11 during and after a 3-day treatment with three oral antibiotics (rifaximin, trimethoprim/sulfamethoxazole, and metronidazole). (C) Unweighted UniFrac principal coordinates analysis of fecal microbiota composition in the samples collected from five healthy human subjects as described in (B). (D) Fecal bacterial load quantified by 16S rRNA gene copy number PCR (per gram of stool) and anaerobic culture [colony-forming units (CFU) per gram of stool)] in healthy human subjects, who received a bowel-cleansing protocol of oral antibiotics (neomycin and vancomycin) for 72 hours and a PEG purge initiated at 36 hours. n = 5, means ± SD; statistical analysis performed using two-tailed paired Student’s t tests of log-transformed data, *P < 0.05, **P < 0.01. qPCR, quatitative PCR.

Fig. 5. Effect on gut microbiota composition of inoculating a murine host with E. coli MP1 strain engineered with a urease operon

(A) Christensen’surea agar plate shows urease activity, indicated by phenol red as a pH indicator, of Ure+E. coli MP1 colonies and Ure−E. coli MP1 colonies 6 hours after inoculation of murine hosts. (B and C) Bacterial load in mouse feces after inoculation of murine hosts with Ure+E. coli MP1 (B) or Ure−E. coli MP1 (C) at the indicated time points after oral gavage. (D) Unweighted UniFrac principal coordinates analysis of day 0 and day 29 fecal microbiota composition using 16S rRNA gene-tagged sequencing in five groups of mice: ABX/PEG, ABX/PEG then gavaged with normal feces (NF), ABX/PEG then gavaged with Ure+E. coli MP1, ABX/PEG then gavaged with Ure−E. coli MP1, and ABX/PEG then gavaged with a mixture of Ure+/Ure−E. coli MP1. (E) Box plot of unweighted UniFrac distances between samples within and between cages and within and between study groups. (F) Taxonomic abundance of bacteria 29 days after inoculation of murine hosts with Ure−E. coli MP1, Ure+E. coli MP1, or a mixture of both.

Fig. 6. Effect of E. coli urease on colitis in a T cell adoptive transfer mouse model of colitis

(A) Bacterial load represented as CFUs per gram of stool in the feces of mice after inoculation with Ure− (black) or Ure+ (red) E. coli MP1 strains. Day 0 is the time point of T cell adoptive transfer. (B) Weight of T cell adoptively transferred Rag−/− mice after inoculation with either Ure− (black) or Ure+ (red) E. coli MP1 strains. Statistical analysis was performed using two-tailed unpaired Student’s t test, *P < 0.05. (C) Mouse colonic and cecal weights, colonic length, and disease activity index (DAI) in T cell adoptively transferred Rag−/− mice 44 days after inoculation with either Ure− or Ure+E. coli MP1 strains. Statistical analysis was performed using two-tailed unpaired Student’s t test, *P ≤ 0.02, **P = 0.002. (D) Photomicrographs of hematoxylin and eosin–stained mouse colon tissue from T cell adoptively transferred Rag−/− mice 44 days after inoculation with either Ure− or Ure+E. coli MP1 strains.